1. Technical Field
The present disclosure relates to X-ray images and, more specifically, to marker detection in X-ray images.
2. Discussion of Related Art
While certain types of X-ray studies such as high-resolution computed tomography (CT) scans can produce exceptionally sharp images in which many different types of matter can be distinguished, it may be difficult to accurately visualize soft tissue and other objects of low-density within conventional X-ray images. This may be especially true of x-ray fluoroscope images that capture a sequence of relatively low-energy x-ray images to provide a moving cine view.
X-ray fluoroscopy plays an important role in interventional imaging, where real-time image data is used to allow for the placement of various foreign objects such as stents and catheters within the circulatory system. During such procedures, radio-opaque contrast agents may be circulated within a patient's bloodstream so that arteries may be clearly visualized. However, in this high-contrast imaging environment it may be especially difficult to distinguish objects and structures other han the arteries within which the radio-opaque contrast agent flows.
In order to more clearly visualize objects such as stents and other objects used during the treatment of atherosclerotic stenosis, stents may be mounted to radio-opaque balloon markers, which may be deployed at both sides of the balloon catheter. Makers may be able to provide more accurate positioning of the stents, provided that the markers can be properly identified from the X-ray imagery. The process of marker identification is generally manually performed by medical practitioners involved in the intervention by careful examination of the imagery.
Identification of markers may play an important role for stent visualization. Accordingly, marker detection may be automated using computerized systems. Automated identification of markers may then be used to perforin automated identification of stents, as the location of the marker may be used to help identify the location of the stents that are marked. Automated identification of stents may then be used by computer aided diagnosis (CAD) systems or for other post-processing for stents. For example, automatic identification of stents may be used in performing stent enhancement, where the appearance of stents within X-ray imagery may be enhanced and signal to noise ratio increased.
In X-ray images, markers may appear circular or elliptical and their radii and axes may be determined by image resolution. Since image resolution is generally known in advance, the marker size is thus known for a given image. Other than knowledge of size and shape of a marker, there are no other special features or patterns that are generally used to help distinguish markers from other structures. In theory, markers have high contrast and unique shape, so they are intended for easy identification within X-ray images. However, for automatic computerized system in practice, it is actually difficult to accurately identify markers owing to factors such as interference from anatomical structures and artificial structures, and the low signal-to-noise ratio nature of fluoroscopy.
Additionally, manual annotation of markers within fluoroscope imagery may be labor intensive and may result in labeling inaccuracy as marker location identification often needs to be accurate to the order of sub-pixel precision.